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$5 USD / time
Flag for PAKISTAN
chitral, pakistan
$5 USD / time
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Tilmeldt januar 9, 2021
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Ayaz M.

@AyazMhmd

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chitral, pakistan
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ML Engineer | Computer vision | NLP

Hey, I am Ayaz Mehmood, A passionate programmer who always engrossed in coding. I Have more than 2 years of experience in python, Machine learning, Computer vision, NLP, Flask and rest API. I am currently working with National Center of Artificial Intelligence as an AI Engineer. In CV, I have experience of Image classification, segmentation, detection and tracking. In NLP, I have experience of State of the art transformer model, GPT etc. and I can work on text classification, language translation, Clustering, NER, Topic Modeling etc.
Freelancer Python Developers Pakistan

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Portefølje indlæg

I have worked on more than 5 projects of yolov5 custom object detection and tracking project
Yolov5 Custom Model training
I have worked on more than 5 projects of yolov5 custom object detection and tracking project
Yolov5 Custom Model training
I have worked on more than 5 projects of yolov5 custom object detection and tracking project
Yolov5 Custom Model training
I have worked on multiple yolo projects on custom data for object detection and segmentation. I have applied yolo on car image, sports data. number plate detection, plant disease detection etc
Yolo custom object detection
I have worked on multiple yolo projects on custom data for object detection and segmentation. I have applied yolo on car image, sports data. number plate detection, plant disease detection etc
Yolo custom object detection
I have worked on multiple yolo projects on custom data for object detection and segmentation. I have applied yolo on car image, sports data. number plate detection, plant disease detection etc
Yolo custom object detection
I have worked on multiple yolo projects on custom data for object detection and segmentation. I have applied yolo on car image, sports data. number plate detection, plant disease detection etc
Yolo custom object detection
This was a project of computer vision on video data in which i worked on 3 anomalous category (bulglary, fighting and explosion) and deployed the model into a flutter app. The app takes real time video through camera and the app tell us about the event. if the event detected was not normal a message is send to a phone number with The event details
Anomaly Detection from Video Streams using Deep learning
This was a project of computer vision on video data in which i worked on 3 anomalous category (bulglary, fighting and explosion) and deployed the model into a flutter app. The app takes real time video through camera and the app tell us about the event. if the event detected was not normal a message is send to a phone number with The event details
Anomaly Detection from Video Streams using Deep learning
This was a project of computer vision on video data in which i worked on 3 anomalous category (bulglary, fighting and explosion) and deployed the model into a flutter app. The app takes real time video through camera and the app tell us about the event. if the event detected was not normal a message is send to a phone number with The event details
Anomaly Detection from Video Streams using Deep learning
This was a project of computer vision on video data in which i worked on 3 anomalous category (bulglary, fighting and explosion) and deployed the model into a flutter app. The app takes real time video through camera and the app tell us about the event. if the event detected was not normal a message is send to a phone number with The event details
Anomaly Detection from Video Streams using Deep learning
This was a project of computer vision on video data in which i worked on 3 anomalous category (bulglary, fighting and explosion) and deployed the model into a flutter app. The app takes real time video through camera and the app tell us about the event. if the event detected was not normal a message is send to a phone number with The event details
Anomaly Detection from Video Streams using Deep learning
This was a project of computer vision on video data in which i worked on 3 anomalous category (bulglary, fighting and explosion) and deployed the model into a flutter app. The app takes real time video through camera and the app tell us about the event. if the event detected was not normal a message is send to a phone number with The event details
Anomaly Detection from Video Streams using Deep learning

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Erfaring

Artificial Intelligence Engineer

National Center of Artificial Intelligence
mar. 2022 - Nuværende
National Center of Artificial intelligence works under the government and we focus on research and product based project. Currently we are working on Medical imaging which includes TB classification and detection, Breast Cancer Classification and segmentation and Brain tumor classification and segmentation. Apart from this, we work on Smart cities project like lane detection, object tracking, distancing etc. In NLP, We work on language translation and text to speech and vice versa projects

Data Scientist

National Center of Cyber security
mar. 2020 - Nuværende
Working as an AI research Scholar with one the prestigious place. We as a team have worked on multiple research and product based project.

Uddannelse

Computer System Engineering

University of Engineering and Technology, Taxila, Pakistan 2017 - 2021
(4 år)

Kvalifikationer

Bs in Engineering

University of Engineering and Technology
2021
I have completed my engineering Degree and currently working as a data scientist in a multinational software house

Publikationer

Multimedia Analysis of Disaster-Related Social Media Data

Mediaeval Competition
In the first part, we Classified the tweeter data as disaster or not from the data shared by the organization In the second part, we specified the location using named entity recognition We have used different modeling technique as the project has two parts. We explored the state-of-the-art model for this and achieved F1 score of 92

Arrhythmia Detection Using Deep Learning Techniques

IEEE Transactions on Biomedical circuits and systems.
Classified ECG signal using a Conventional Neural Network (CNN) & LSTM.

Anomaly Detection from Video Streams Using Deep Learning techniques

IEEE
It was my final year project and we worked on 4 anomalous category from video data and classified the videos. we achieved 82% of accuracy

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